Risk and crisis communication (RCC) is a current ethical issue subject to controversy, mainly due to the tension between individual liberty (a core component of fairness) and effectiveness. In this paper we propose a consistent definition of the RCC process in public health emergencies (PHERCC), which comprises six key elements: evidence, initiator, channel, publics, message, and feedback. Based on these elements and on a detailed analysis of their role in PHERCC, we present an ethical framework to help design, govern and (...) evaluate PHERCC strategies. The framework aims to facilitate RCC, incorporating effectiveness, autonomy, and fairness. It comprises five operational ethical principles: openness, transparency, inclusivity, understandability, and privacy. The resulting matrix helps understanding the interplay between the PHERCC process and the principles of the framework. The paper includes suggestions and recommendations for the implementation of the PHERCC matrix. (shrink)

Designing and manufacturing medical devices for specific patients is becoming increasingly feasible with developments in 3D printing and 3D imaging software. This raises the question of how patient-specific devices can be evaluated, since our ‘gold standard’ method for evaluation, the randomised controlled trial, requires that an intervention is standardised across a number of individuals in an experimental group. I distinguish several senses of patient-specific device, and focus the discussion on understanding the problem of variations between instances of an intervention for (...) RCT evaluation. I argue that, despite initial appearances, it is theoretically possible to use RCTs to evaluate some patient-specific medical devices. However, the argument reveals significant difficulties for ensuring the validity of such trials, with implications for how we should think about methods of evidence gathering and regulatory approaches for these technologies. (shrink)

Proponents of evidence-based medicine and some philosophers of science seem to agree that knowledge of mechanisms can help solve the problem of applying results of controlled studies to target populations (‘the problem of extrapolation’). We describe the problem of extrapolation, characterize mechanisms, and outline how mechanistic knowledge might be used to solve the problem. Our main thesis is that there are four often overlooked problems with using mechanistic knowledge to solve the problem of extrapolation. First, our understanding of mechanisms is (...) often (and arguably, likely to remain) incomplete. Secondly, knowledge of mechanisms is not always applicable outside the tightly controlled laboratory conditions in which it is gained. Thirdly, mechanisms can behave paradoxically. Fourthly, as Daniel Steel points out, using mechanistic knowledge faces the problem of the ‘extrapolator’s circle’. At the same time, when the problems with mechanistic knowledge have been addressed, such knowledge can and should be used to mitigate (nothing can entirely solve) the problem of extrapolation. (shrink)

There are some philosophical questions that can be answered without attention to the social context in which evidence is produced and distributed.ing away from social context is an excellent way to ignore messy details and lay bare the underlying structure of the limits of inference. Idealization is entirely appropriate when one is essentially asking: In the best of all possible worlds, what am I entitled to infer? Yet, philosophers’ concerns often go beyond this domain. As an example I examine the (...) debate on mechanistic evidence and then reevaluate a canonical case study in this debate. I show that for the assessment of actual evidence, produced in a world that is far from ideal, omission of the social aspects of medical epistemology leads philosophers to draw the wrong lessons from cases they take as paradigmatic cases for their views. I close by arguing that social epistemology provides an avenue to incorporate these complications and provides the necessary framework to understand medical evidence. (shrink)

Arguments that medical decision making should rely on a variety of evidence often begin from the claim that meta-analysis has been shown to be problematic. In this paper, I first examine Stegenga’s argument that meta-analysis requires multiple decisions and thus fails to provide an objective ground for medical decision making. Next, I examine three arguments from social epistemologists that contend that meta-analyses are systematically biased in ways not appreciated by standard epistemology. In most cases I show that critiques of meta-analysis (...) fail to account for the full range of meta-analytic procedures. In the remainder of cases, I argue that the critiques identify problems that do not uniquely cut against meta-analysis. I close by suggesting one reason why it may be pragmatically rational to violate the principle of total evidence and by outlining the criteria for a successful argument against meta-analysis. A set of criteria I contend remain unmet. (shrink)

This paper critically analyzes Sherrilyn Roush's (Tracking truth: knowledge, evidence and science, 2005) definition of evidence and especially her powerful defence that in the ideal, a claim should be probable to be evidence for anything. We suggest that Roush treats not one sense of 'evidence' but three: relevance, leveraging and grounds for knowledge; and that different parts of her argument fare differently with respect to different senses. For relevance, we argue that probable evidence is sufficient but not necessary for Roush's (...) own two criteria of evidence to be met. With respect to grounds for knowledge, we agree that high probability evidence is indeed ideal for the central reason Roush gives: When believing a hypothesis on the basis of e it is desirable that e be probable. But we maintain that her further argument that Bayesians need probable evidence to warrant the method they recommend for belief revision rests on a mistaken interpretation of Bayesian conditionalization. Moreover, we argue that attempts to reconcile Roush's arguments with Bayesianism fail. For leveraging, which we agree is a matter of great importance, the requirement that evidence be probable suffices for leveraging to the probability of the hypothesis if either one of Roush's two criteria for evidence are met. Insisting on both then seems excessive. To finish, we show how evidence, as Roush defines it, can fail to track the hypothesis. This can remedied by adding a requirement that evidence be probable, suggesting another rationale for taking probable evidence as ideal—but only for a grounds-for-knowledge sense of evidence. (shrink)

The thesis consists of an introduction and two papers. In the introduction a brief historical survey of empirical investigations into the effectiveness of medicinal interventions is given. Also, the main ideas of the EBM movement are presented. Both included papers can be viewed as investigations into the reasonableness of EBM and its hierarchies of evidence. Paper I: Typically, in a clinical trial patients with specified symptoms are given either of two or more predetermined treatments. Health endpoints in these groups are (...) then compared using statistical methods. Concerns have been raised, not least from adherents of so-called alternative medicine, that clinical trials do not offer reliable evidence for some types of treatment, in particular for highly individualized treatments, for example traditional homeopathy. It is argued that such concerns are unfounded. There are two minimal conditions related to the nature of the treatments that must be fulfilled for evaluability in a clinical trial, namely the proper distinction of the two treatment groups and the elimination of confounding variables or variations. These are delineated, and a few misunderstandings are corrected. It is concluded that the conditions do not preclude the testing of alternative medicine, whether individualized or not. Paper II: Traditionally, mechanistic reasoning has been assigned a negligible role in standard EBM literature, although some recent authors have argued for an upgrading. Even so, mechanistic reasoning that has received attention has almost exclusively been positive -- both in an epistemic sense of claiming that there is a mechanistic chain and in a health-related sense of there being claimed benefits for the patient. Negative mechanistic reasoning has been neglected, both in the epistemic and in the health-related sense. I distinguish three main types of negative mechanistic reasoning and subsume them under a new definition of mechanistic reasoning in the context of assessing medical interventions. Although this definition is wider than a previous suggestion in the literature, there are still other instances of reasoning that concern mechanisms but do not count as mechanistic reasoning. One of the three distinguished types, which is negative only in the health-related sense, has a corresponding positive counterpart, whereas the other two, which are epistemically negative, do not have such counterparts, at least not that are particularly interesting as evidence. Accounting for negative mechanistic reasoning in EBM is therefore partly different from accounting for positive mechanistic reasoning. Each negative type corresponds to a range of evidential strengths, and it is argued that there are differences with respect to the typical strengths. The variety of negative mechanistic reasoning should be acknowledged in EBM, and presents a serious challenge to proponents of so-called medical hierarchies of evidence. (shrink)

I aim to show that the failure of the California Class Size Reduction initiative highlights an important class of situations in the application of evidence to policy. There are some circumstances in which the implementation of a policy will be self-defeating. The introduction of the factor assumed to have causal efficacy into the target population can lead to changes in the conditions of the target population that amount to interfering factors. Because these interfering factors are a direct result of the (...) policy implementation they should be relatively easy to predict, and so part of the tricky issue of judging where evidence is relevant should in some circumstances be relatively straightforward. -/- The failure of the California Class Size Reduction initiative also shows how important it is to identify the correct causal factor. The more accurate the attribution of causality, the less susceptible it will be to interfering factors and breaks in the causal chain. (shrink)

I aim to show that the failure of the California Class Size Reduction initiative highlights an important class of situations in the application of evidence to policy. There are some circumstances in which the implementation of a policy will be self-defeating. The introduction of the factor assumed to have causal efficacy into the target population can lead to changes in the conditions of the target population that amount to interfering factors. Because these interfering factors are a direct result of the (...) policy implementation they should be relatively easy to predict, and so part of the tricky issue of judging where evidence is relevant should in some circumstances be relatively straightforward. The failure of the California Class Size Reduction initiative also shows how important it is to identify the correct causal factor. The more accurate the attribution of causality, the less susceptible it will be to interfering factors and breaks in the causal chai. (shrink)